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Proceedings Paper

Relational data clustering with incomplete data
Author(s): Richard J. Hathaway; Dessa D. Overstreet; Thomas E. Murphy; James C. Bezdek
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Paper Abstract

We consider the problem of clustering a set of objects which are represented by rational data in the form of a dissimilarity matrix which has missing values. Three methods are developed to estimate the missing values, all based on simple triangle inequality-based approximation schemes. With few exceptions, any relational clustering algorithm can then be applied to the completed data matrix to obtain nice clusters. We illustrate our approach by clustering incomplete data built from several data sets. The primary clustering method chosen for our numerical experiments is the non-Euclidean relational fuzzy c-means algorithm. Our examples show that satisfactory clusters can still be obtained even when roughly half of the distance values are missing before completion.

Paper Details

Date Published: 21 March 2001
PDF: 8 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421178
Show Author Affiliations
Richard J. Hathaway, Georgia Southern Univ. (United States)
Dessa D. Overstreet, Equifax (United States)
Thomas E. Murphy, Georgia Southern Univ. (United States)
James C. Bezdek, Univ. of West Florida (United States)

Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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